NONPARAMETRIC EULER EQUATION IDENTIFICATION AND ESTIMATION
Juan Carlos Escanciano (),
Arthur Lewbel (),
Oliver Linton () and
Econometric Theory, 2021, vol. 37, issue 5, 851-891
We consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption-based asset pricing Euler equations. Ours is the first paper to prove nonparametric identification of Euler equations under low level conditions (without imposing functional restrictions or just assuming completeness). We also propose a novel nonparametric estimator based on our identification analysis, which combines standard kernel estimation with the computation of a matrix eigenvector problem. Our estimator avoids the ill-posed inverse issues associated with nonparametric instrumental variables estimators. We derive limiting distributions for our estimator and for relevant associated functionals. A Monte Carlo experiment shows a satisfactory finite sample performance for our estimators.
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Working Paper: Nonparametric Euler Equation Identification and Estimation (2020)
Working Paper: Nonparametric Euler Equation Identi?cation and Estimation (2020)
Working Paper: Nonparametric Euler Equation Identification andEstimation (2015)
Working Paper: Nonparametric Euler equation identification and estimation (2015)
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